输入类型（mpsfloattype）和重量类型（Torch.floattensor）应相同

Question

我正在尝试运行此

libs Freeze：

>pip3 freeze
anyio @ file:///private/tmp/jupyterlab--anyio-20211211-70040-1yv1wmx/anyio-3.4.0
appnope==0.1.2
argon2-cffi @ file:///private/tmp/jupyterlab--argon2-cffi-20211211-70040-1er07d0/argon2-cffi-21.2.0
argon2-cffi-bindings @ file:///private/tmp/jupyterlab--argon2-cffi-bindings-20211211-70040-o64kwi/argon2-cffi-bindings-21.2.0
asttokens==2.0.5
attrs @ file:///private/tmp/jupyterlab--attrs-20211211-70040-6u3qxt/attrs-21.2.0
Babel==2.9.1
backcall @ file:///private/tmp/jupyterlab--backcall-20211211-70040-acdr42/backcall-0.2.0
beniget==0.4.1
black==21.12b0
bleach==4.1.0
certifi==2022.5.18.1
cffi==1.15.0
charset-normalizer==2.0.12
click==8.0.3
cycler==0.10.0
Cython==0.29.24
debugpy @ file:///private/tmp/jupyterlab--debugpy-20211211-70040-2j9lay/debugpy-1.5.1
decorator==5.1.0
defusedxml @ file:///private/tmp/jupyterlab--defusedxml-20211211-70040-uowur4/defusedxml-0.7.1
entrypoints @ file:///private/tmp/jupyterlab--entrypoints-20211211-70040-1r2y5g4/entrypoints-0.3
et-xmlfile==1.1.0
executing==0.8.2
finnhub-python==2.4.5
gast==0.5.2
GDAL==3.4.0
gensim==4.1.2
graphviz==0.19.1
idna==3.3
imageio==2.13.5
ipykernel==6.6.0
ipython==7.30.1
ipython-genutils==0.2.0
ipywidgets==7.6.5
jedi==0.18.1
Jinja2==3.0.3
joblib==1.1.0
json5==0.9.6
jsonschema @ file:///private/tmp/jupyterlab--jsonschema-20211211-70040-1np642r/jsonschema-4.2.1
jupyter==1.0.0
jupyter-client==7.1.0
jupyter-console==6.4.0
jupyter-core==4.9.1
jupyter-server @ file:///private/tmp/jupyterlab--jupyter-server-20211211-70040-1u7h7vl/jupyter_server-1.13.1
jupyterlab @ file:///private/tmp/jupyterlab-20211211-70040-1ltrjpx/jupyterlab-3.2.5
jupyterlab-pygments==0.1.2
jupyterlab-server @ file:///private/tmp/jupyterlab--jupyterlab-server-20211211-70040-iufjhi/jupyterlab_server-2.8.2
jupyterlab-widgets==1.0.2
kiwisolver==1.3.2
lxml==4.6.3
MarkupSafe==2.0.1
matplotlib==3.4.3
matplotlib-inline==0.1.3
midi @ git+https://github.com/vishnubob/python-midi.git@abb85028c97b433f74621be899a0b399cd100aaa
midi-to-dataframe @ git+https://github.com/TaylorPeer/midi-to-dataframe@35347f787f01a2326234ad278d8c40bee3817f1d
mido==1.2.10
mistune==0.8.4
multitasking==0.0.9
mypy-extensions==0.4.3
nbclassic @ file:///private/tmp/jupyterlab--nbclassic-20211211-70040-1fah2fe/nbclassic-0.3.4
nbclient @ file:///private/tmp/jupyterlab--nbclient-20211211-70040-ptwp5d/nbclient-0.5.9
nbconvert==6.3.0
nbformat==5.1.3
nest-asyncio @ file:///private/tmp/jupyterlab--nest-asyncio-20211211-70040-72pz5e/nest_asyncio-1.5.4
networkx==2.6.3
notebook==6.4.6
numpy==1.23.0rc1
openpyxl==3.0.9
packaging @ file:///private/tmp/jupyterlab--packaging-20211211-70040-1f14ddt/packaging-21.3
pandas==1.4.2
pandocfilters==1.5.0
parso==0.8.3
pathspec==0.9.0
pexpect==4.8.0
pickleshare==0.7.5
Pillow==9.1.1
platformdirs==2.4.1
ply==3.11
prometheus-client==0.12.0
prompt-toolkit @ file:///private/tmp/jupyterlab--prompt-toolkit-20211211-70040-hcpjwc/prompt_toolkit-3.0.24
ptyprocess @ file:///private/tmp/jupyterlab--ptyprocess-20211211-70040-wjbvpa/ptyprocess-0.7.0
pure-eval==0.2.1
pybind11==2.8.0
pycparser==2.21
Pygments==2.10.0
pyparsing==3.0.6
pyrsistent @ file:///private/tmp/jupyterlab--pyrsistent-20211211-70040-1fnadg/pyrsistent-0.18.0
python-dateutil==2.8.2
pythran==0.10.0
pytz==2022.1
PyWavelets==1.2.0
PyYAML==6.0
pyzmq @ file:///private/tmp/jupyterlab--pyzmq-20211211-70040-2xtuon/pyzmq-22.3.0
qtconsole==5.2.2
QtPy==2.0.0
requests==2.27.1
scikit-image==0.19.1
scikit-learn==1.1.dev0
scipy==1.8.1
seaborn==0.11.2
Send2Trash==1.8.0
six==1.16.0
smart-open==5.2.1
sniffio @ file:///private/tmp/jupyterlab--sniffio-20211211-70040-wu3dri/sniffio-1.2.0
squarify==0.4.3
stack-data==0.1.4
terminado @ file:///private/tmp/jupyterlab--terminado-20211211-70040-dw1vl6/terminado-0.12.1
testpath @ file:///private/tmp/jupyterlab--testpath-20211211-70040-895z1/testpath-0.5.0
threadpoolctl==3.0.0
tifffile==2021.11.2
tomli==1.2.3
torch==1.13.0.dev20220528
torchaudio==0.11.0
torchsummary==1.5.1
torchtext==0.10.0
torchvision==0.14.0a0+f0f8a3c
torchviz==0.0.2
tornado==6.1
tqdm==4.62.3
traitlets @ file:///private/tmp/jupyterlab--traitlets-20211211-70040-ru76xv/traitlets-5.1.1
typing_extensions==4.2.0
urllib3==1.26.9
wcwidth==0.2.5
webencodings==0.5.1
websocket-client==1.2.3
wget==3.2
widgetsnbextension==3.5.2
yfinance==0.1.64

在代码中，AM设置device = Torch.device（'MPS'）

在此行中：history = [evaliate（deasuate） ，有效_dl）] AM获取运行时错误

输入类型（MPSFloatType）和权重类型（Torch.floattensor）应该是相同的

跟踪：

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<timed exec> in <module>

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     25         def decorate_context(*args, **kwargs):
     26             with self.clone():
---> 27                 return func(*args, **kwargs)
     28         return cast(F, decorate_context)
     29 

/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/1143432410.py in evaluate(model, val_loader)
      3 def evaluate(model, val_loader):
      4     model.eval()
----> 5     outputs = [model.validation_step(batch) for batch in val_loader]
      6     return model.validation_epoch_end(outputs)
      7 

/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/1143432410.py in <listcomp>(.0)
      3 def evaluate(model, val_loader):
      4     model.eval()
----> 5     outputs = [model.validation_step(batch) for batch in val_loader]
      6     return model.validation_epoch_end(outputs)
      7 

/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/446280773.py in validation_step(self, batch)
     16     def validation_step(self, batch):
     17         images, labels = batch
---> 18         out = self(images)                   # Generate prediction
     19         loss = F.cross_entropy(out, labels)  # Calculate loss
     20         acc = accuracy(out, labels)          # Calculate accuracy

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1128         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1129                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130             return forward_call(*input, **kwargs)
   1131         # Do not call functions when jit is used
   1132         full_backward_hooks, non_full_backward_hooks = [], []

/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/3789274317.py in forward(self, xb)
     29 
     30     def forward(self, xb): # xb is the loaded batch
---> 31         out = self.conv1(xb)
     32         out = self.conv2(out)
     33         out = self.res1(out) + out

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1128         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1129                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130             return forward_call(*input, **kwargs)
   1131         # Do not call functions when jit is used
   1132         full_backward_hooks, non_full_backward_hooks = [], []

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/container.py in forward(self, input)
    137     def forward(self, input):
    138         for module in self:
--> 139             input = module(input)
    140         return input
    141 

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1128         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1129                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130             return forward_call(*input, **kwargs)
   1131         # Do not call functions when jit is used
   1132         full_backward_hooks, non_full_backward_hooks = [], []

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/conv.py in forward(self, input)
    457 
    458     def forward(self, input: Tensor) -> Tensor:
--> 459         return self._conv_forward(input, self.weight, self.bias)
    460 
    461 class Conv3d(_ConvNd):

/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    453                             weight, bias, self.stride,
    454                             _pair(0), self.dilation, self.groups)
--> 455         return F.conv2d(input, weight, bias, self.stride,
    456                         self.padding, self.dilation, self.groups)
    457 

RuntimeError: Input type (MPSFloatType) and weight type (torch.FloatTensor) should be the same

MPS仍然是新的，并且正在尝试找出原因在这里，欢迎任何建议， 如果将火炬设备设置为CPU，则代码运行正常 - 只需花费太多时间。

谢谢， 深卡马尔·辛格

Answer 1

我的猜测是该模型尚未放在MPS设备上。

如果将模型放置在MPS设备上（通过调用model.to（device）），您的代码是否有效？

Answer 2

我遇到了同样的情况。
通过添加将其求解到（'MPS'） 模型，例如：

model = torchvision.models.resnet50(pretrained=True).to('mps').to(torch.float16)